


'''simple docstring'''



import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed





class   UpperCAmelCase_    (a_    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=10_24     ,	SCREAMING_SNAKE_CASE_=10_24     ,	SCREAMING_SNAKE_CASE_=3.6      )							->	Dict:
         __lowerCamelCase  :      Union[str, Any]														=						tokenizer
         __lowerCamelCase  :      str														=						tokenizer.bos_token_id
         __lowerCamelCase  :      str														=						dataset
         __lowerCamelCase  :      Optional[Any]														=						seq_length
         __lowerCamelCase  :      int														=						seq_length * chars_per_token * num_of_sequences







    def __iter__(				self      )							->	str:
         __lowerCamelCase  :      Any														=						iter(self.dataset      )
         __lowerCamelCase  :      int														=						True
         while more_examples:
              __lowerCamelCase							,			__lowerCamelCase  :      List[Any]														=						[], 0
              while True:
                   if buffer_len >= self.input_characters:
                        break
                   try:
                        buffer.append(next(UpperCamelCase_      )['content']      )
                        buffer_len += len(buffer[-1]      )
                   except StopIteration:
                        __lowerCamelCase  :      int														=						False
                        break
              __lowerCamelCase  :      int														=						tokenizer(UpperCamelCase_     ,	truncation=UpperCamelCase_      )['input_ids']
              __lowerCamelCase  :      Tuple														=						[]
              for tokenized_input in tokenized_inputs:
                   all_token_ids.extend(tokenized_input + [self.concat_token_id]      )
              for i in range(0     ,	len(UpperCamelCase_      )     ,	self.seq_length      ):
                   __lowerCamelCase  :      List[Any]														=						all_token_ids[i : i + self.seq_length]
                   if len(UpperCamelCase_      ) == self.seq_length:
                        yield torch.tensor(UpperCamelCase_      )







def      UpperCAmelCase__   (      UpperCAmelCase_       :							Any				)	->						Any:
     __lowerCamelCase  :      Union[str, Any]														=						{'streaming': True}
     __lowerCamelCase  :      Optional[Any]														=						load_dataset(args.dataset_name ,       split='train' ,       **__snake_case				)
     __lowerCamelCase  :      Union[str, Any]														=						ConstantLengthDataset(__snake_case ,       __snake_case ,       seq_length=args.seq_length				)
     __lowerCamelCase  :      Any														=						DataLoader(__snake_case ,       batch_size=args.batch_size				)
     return eval_dataloader



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Union[str, Any]				)	->						List[Any]:
     model.eval()
     __lowerCamelCase  :      Dict														=						[]
     for step, batch in enumerate(__snake_case				):
          with torch.no_grad():
               __lowerCamelCase  :      Union[str, Any]														=						model(__snake_case ,       labels=__snake_case				)
          __lowerCamelCase  :      int														=						outputs.loss.repeat(args.batch_size				)
          losses.append(accelerator.gather(__snake_case				)				)

          if args.max_eval_steps > 0 and step >= args.max_eval_steps:
               break
     __lowerCamelCase  :      int														=						torch.mean(torch.cat(__snake_case				)				)
     try:
          __lowerCamelCase  :      Union[str, Any]														=						torch.exp(__snake_case				)
     except OverflowError:
          __lowerCamelCase  :      Union[str, Any]														=						float('inf'				)
     return loss.item(), perplexity.item()


# Setup Accelerator
A__						:   int				=    Accelerator()

# Parse configuration
A__						:   Optional[Any]				=    HfArgumentParser(EvaluationArguments)
A__						:   Dict				=    parser.parse_args()
set_seed(args.seed)

# Logging
A__						:   str				=    logging.getLogger(__name__)
logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)

# Load model and tokenizer
A__						:   List[str]				=    AutoModelForCausalLM.from_pretrained(args.model_ckpt)
A__						:   str				=    AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
A__						:   Tuple				=    create_dataloader(args)

# Prepare everything with our `accelerator`.
A__						:   int				=    accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
A__						:   Union[str, Any]				=    evaluate(args)

logger.info(f'''loss/eval: {eval_loss}, perplexity: {perplexity}''')














"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case : int     		=		{}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case : str     		=		["""NllbTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case : Dict     		=		["""NllbTokenizerFast"""]


if TYPE_CHECKING:
 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_nllb import NllbTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_nllb_fast import NllbTokenizerFast

else:
 import sys

 snake_case : Dict     		=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)